Meta Representation Transformation for Low-resource Cross-lingual Learning

Related tags

Deep LearningMetaXL
Overview

MetaXL: Meta Representation Transformation for Low-resource Cross-lingual Learning

This repo hosts the code for MetaXL, published at NAACL 2021.

[MetaXL: Meta Representation Transformation for Low- resource Cross-lingual Learning] (https://arxiv.org/pdf/2104.07908.pdf)

Mengzhou Xia, Guoqing Zheng, Subhabrata Mukherjee, Milad Shokouhi, Graham Neubig, Ahmed Hassan Awadallah

NAACL 2021

MetaXL is a meta-learning framework that learns a main model and a relatively small structure, called representation transformation network (RTN) through a bi-level optimization procedure with the goal to transform representations from auxiliary languages such that it benefits the target task the most.

Data

Please download [WikiAnn] (https://github.com/afshinrahimi/mmner), [MARC] (https://registry.opendata.aws/amazon-reviews-ml/), [SentiPers] (https://github.com/phosseini/sentipers) and [Sentiraama] (https://ltrc.iiit.ac.in/showfile.php?filename=downloads/sentiraama/) on its corresponding. Please refer to data/data_index.txt for data splits.

Scripts

The following script shows how to run metaxl on the named entity recognition task on Quechua.

python3 mtrain.py \
      --data_dir data_dir \
      --bert_model xlm-roberta-base \
      --tgt_lang qa \
      --task_name panx \
      --train_max_seq_length 200 \
      --max_seq_length 512 \
      --epochs 20 \
      --batch_size 10 \
      --method metaxl \
      --output_dir output_dir \
      --warmup_proportion 0.1 \
      --main_lr 3e-05 \
      --meta_lr 1e-06 \
      --train_size 1000\
      --target_train_size 100 \
      --source_languages en \
      --source_language_strategy specified \
      --layers 12 \
      --struct perceptron \
      --tied  \
      --transfer_component_add_weights \
      --tokenizer_dir None \
      --bert_model_type ori \
      --bottle_size 192 \
      --portion 2 \
      --data_seed 42  \
      --seed 11 \
      --do_train  \
      --do_eval 

The following script shows how to run metaxl on the sentiment analysis task on fa.

python3 mtrain.py  \
		--data_dir data_dir \
		--task_name sent \
		--bert_model xlm-roberta-base \
		--tgt_lang fa \
		--train_max_seq_length 256 \
		--max_seq_length 256 \
		--epochs 20 \
		--batch_size 10 \
		--method metaxl \
		--output_dir ${output_dir} \
		--warmup_proportion 0.1 \
		--main_lr 3e-05 \
		--meta_lr 1e-6 \
		--train_size 1000 \
		--target_train_size 100 \
		--source_language_strategy specified  \
		--source_languages en \
		--layers 12 \
		--struct perceptron \
		--tied  \
		--transfer_component_add_weights \
		--tokenizer_dir None  \
		--bert_model_type ori  \
		--bottle_size 192  \
		--portion 2 	\
		--data_seed 42 \
		--seed 11  \
		--do_train  \
		--do_eval

Citation

If you find MetaXL useful, please cite the following paper

@inproceedings{xia2021metaxl,
  title={MetaXL: Meta Representation Transformation for Low-resource Cross-lingual Learning},
  author={Mengzhou, Xia and Zheng, Guoqing and Mukherjee, Subhabrata and Shokouhi, Milad and Newbig, Graham and Awadallah, Ahmed Hassan},
  journal={NAACL},
  year={2021},
}

This repository is released under MIT License. (See LICENSE)

Owner
Microsoft
Open source projects and samples from Microsoft
Microsoft
TransCD: Scene Change Detection via Transformer-based Architecture

TransCD: Scene Change Detection via Transformer-based Architecture

wangzhixue 29 Dec 11, 2022
A fast, distributed, high performance gradient boosting (GBT, GBDT, GBRT, GBM or MART) framework based on decision tree algorithms, used for ranking, classification and many other machine learning tasks.

Light Gradient Boosting Machine LightGBM is a gradient boosting framework that uses tree based learning algorithms. It is designed to be distributed a

Microsoft 14.5k Jan 08, 2023
A Python Package for Portfolio Optimization using the Critical Line Algorithm

PyCLA A Python Package for Portfolio Optimization using the Critical Line Algorithm Getting started To use PyCLA, clone the repo and install the requi

19 Oct 11, 2022
Python Multi-Agent Reinforcement Learning framework

- Please pay attention to the version of SC2 you are using for your experiments. - Performance is *not* always comparable between versions. - The re

whirl 1.3k Jan 05, 2023
An example project demonstrating how the Autonomous Learning Library can be used to build new reinforcement learning agents.

About This repository shows how Autonomous Learning Library can be used to build new reinforcement learning agents. In particular, it contains a model

Chris Nota 5 Aug 30, 2022
“英特尔创新大师杯”深度学习挑战赛 赛道3:CCKS2021中文NLP地址相关性任务

ccks2021-track3 CCKS2021中文NLP地址相关性任务-赛道三-冠军方案 团队:我的加菲鱼- wodejiafeiyu 初赛第二/复赛第一/决赛第一 前言 19年开始,陆陆续续参加了一些比赛,拿到过一些top,比较懒一直都没分享过,这次比较幸运又拿了top1,打算分享下 分类的任务

shaochenjie 131 Dec 31, 2022
Deep Learning for Natural Language Processing SS 2021 (TU Darmstadt)

Deep Learning for Natural Language Processing SS 2021 (TU Darmstadt) Task Training huge unsupervised deep neural networks yields to strong progress in

2 Aug 05, 2022
Multi Task RL Baselines

MTRL Multi Task RL Algorithms Contents Introduction Setup Usage Documentation Contributing to MTRL Community Acknowledgements Introduction M

Facebook Research 171 Jan 09, 2023
Can we learn gradients by Hamiltonian Neural Networks?

Can we learn gradients by Hamiltonian Neural Networks? This project was carried out as part of the Optimization for Machine Learning course (CS-439) a

2 Aug 22, 2022
Unconstrained Text Detection with Box Supervisionand Dynamic Self-Training

SelfText Beyond Polygon: Unconstrained Text Detection with Box Supervisionand Dynamic Self-Training Introduction This is a PyTorch implementation of "

weijiawu 34 Nov 09, 2022
Finetuning Pipeline

KLUE Baseline Korean(한국어) KLUE-baseline contains the baseline code for the Korean Language Understanding Evaluation (KLUE) benchmark. See our paper fo

74 Dec 13, 2022
A deep learning CNN model to identify and classify and check if a person is wearing a mask or not.

Face Mask Detection The Model is designed to check if any human is wearing a mask or not. Dataset Description The Dataset contains a total of 11,792 i

1 Mar 01, 2022
Python Implementation of algorithms in Graph Mining, e.g., Recommendation, Collaborative Filtering, Community Detection, Spectral Clustering, Modularity Maximization, co-authorship networks.

Graph Mining Author: Jiayi Chen Time: April 2021 Implemented Algorithms: Network: Scrabing Data, Network Construbtion and Network Measurement (e.g., P

Jiayi Chen 3 Mar 03, 2022
A scikit-learn compatible neural network library that wraps PyTorch

A scikit-learn compatible neural network library that wraps PyTorch. Resources Documentation Source Code Examples To see more elaborate examples, look

4.9k Dec 31, 2022
Differentiable simulation for system identification and visuomotor control

gradsim gradSim: Differentiable simulation for system identification and visuomotor control gradSim is a unified differentiable rendering and multiphy

105 Dec 18, 2022
FAMIE is a comprehensive and efficient active learning (AL) toolkit for multilingual information extraction (IE)

FAMIE: A Fast Active Learning Framework for Multilingual Information Extraction

18 Sep 01, 2022
A python implementation of Physics-informed Spline Learning for nonlinear dynamics discovery

PiSL A python implementation of Physics-informed Spline Learning for nonlinear dynamics discovery. Sun, F., Liu, Y. and Sun, H., 2021. Physics-informe

Fangzheng (Andy) Sun 8 Jul 13, 2022
Simple (but Strong) Baselines for POMDPs

Recurrent Model-Free RL is a Strong Baseline for Many POMDPs Welcome to the POMDP world! This repo provides some simple baselines for POMDPs, specific

Tianwei V. Ni 172 Dec 29, 2022
Pytorch implementation of Depth-conditioned Dynamic Message Propagation forMonocular 3D Object Detection

DDMP-3D Pytorch implementation of Depth-conditioned Dynamic Message Propagation forMonocular 3D Object Detection, a paper on CVPR2021. Instroduction T

Li Wang 32 Nov 09, 2022
Project repo for Learning Category-Specific Mesh Reconstruction from Image Collections

Learning Category-Specific Mesh Reconstruction from Image Collections Angjoo Kanazawa*, Shubham Tulsiani*, Alexei A. Efros, Jitendra Malik University

438 Dec 22, 2022